Hand gesture recognition and tracking based on distributed locally linear embedding
Image and Vision Computing
Estimating per-pixel thematic uncertainty in remote sensing classifications
International Journal of Remote Sensing
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
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The texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed as a Gaussian mixture model. The feature-label interactions and a set of label-label interactions are represented on a constraint satisfaction neural network. A stochastic relaxation strategy is used to obtain an optimal classification of textures in an image. The advantage of this approach is that all classes in an image are determined simultaneously, similar to human perception of textures in an image